Popular GitHub Repositories for Epsilon

CSE 571 Artificial Intelligence

implement basic and contextual MAB algorithms for recommendation system

Multi-armed bandit algorithm with tensorflow and 11 policies

The goal of this project is to build an RL-based algorithm that can help cab drivers maximize their profits by improving their decision-making process on the field. Taking long-term profit as the goal, a method is proposed based on reinforcement learning to optimize taxi driving strategies for profit maximization. This optimization problem is formulated as a Markov Decision Process i.e. MDP.

Multi-Agent Deep Recurrent Q-Learning with Bayesian epsilon-greedy on AirSim simulator

[TNNLS] PGDQN: A generalized and efficient preference-guided epsilon-greedy policy equipped DQN for Atari and Autonomous Driving

Tic tac toe game intelligence agent using reinforcement learning

a Python-based platformer infused with Q-Learning and dynamic level creation from simple JSON files.

Explore key RL algorithms with detailed explanations and fully commented Python code implementations

Adversarial Attack using a DCGAN

Build an RL (Reinforcement Learning) agent that learns to play Numerical Tic-Tac-Toe. The agent learns the game by Q-Learning.

Epsilon-greedy solution to the multiarmed-bandit problem, with recency bias

RL algorithms for pygame version of Flappy Bird

This project implements a Deep Reinforcement Learning (DRL) agent to play the classic Atari game Space Invaders using the DQN (Deep Q-Network) algorithm. The project includes a Jupyter notebook (space_invaders.ipynb) that walks through the process of training and testing the agent.

In this notebook, i have tried to appy KMeans, Hierarchical and DBSCAN clustering along PCA. The dataset used is Mall_Customers. In DBSCAN, certain type of Heatmaps are used to find the Epsilon and min_samples value which have performed quite well in identifying the correct number of clusters.

A set of tools for machine learning (for the current day, there are active learning utilities and implementations of some stacking-based techniques).

Improved Bot Learning process on Atari games by using Transfer Learning. An Extension of Playing Atari with Reinforcement Learning. Part of CS677 NJIT Final Project.

?Deep learning model is presented to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards in RL Pong environment.

parameter optimization of a reinforcement learning deep Q network with memory replay buffer using genetic algorithm in the snake game. base code for snake env from codecamp

Epsilon-greedy, UCB and Thomson sampling for Bernoulli rewards in pure jitted Jax